Authors:
Giovanni Acampora
1
;
Autilia Vitiello
2
;
Ciro Di Nunzio
3
;
Maurizio Saliva
4
and
Luciano Garofano
5
Affiliations:
1
Nottingham Trent University, United Kingdom
;
2
University of Salerno, Italy
;
3
University ”Magna Graecia” of Catanzaro, Italy
;
4
Azienda Sanitaria Locale ASL Napoli 3 Sud, Italy
;
5
Arma dei Carabinieri and Italian Academy of Forensic Sciences, Italy
Keyword(s):
Forensic Intelligence, Pattern Recognition, Image Processing, Fuzzy Reasoning
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Pattern Recognition: Fuzzy Clustering and Classifiers
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Bloodstain pattern analysis (BPA) is a forensic discipline that plays a key role in tracing events which caused
a bloodshed at a crime scene. Indeed, BPA supports worldwide investigation agencies (US FBI, Italian Carabinieri
and so on) in interpreting the morphology and distribution of bloodspots at a crime scene in order to
enable a potentially complete reconstruction of the dynamics of the act of violence with a consequent identification
of potential suspects for that crime. However, in spite of its importance, this forensic discipline is
still based on completely manual approaches, making the analysis of a crime scene long, tedious and potentially
imperfect. This position paper is aimed at proving that computational intelligence methodologies can
be efficiently integrated with image processing techniques to support forensic investigators in increasing their
performance in examining bloodstains, both in terms of time and accuracy of analysis. A preliminary study
involving
the application of fuzzy clustering has been carried out in order to validate our opinion and stimulate
computational intelligence community to face this new challenge towards a formal definition of Forensic
Intelligence.
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